Using convolutional neural network for matching cost computation in stereo matching

Aleksei Denisov; Yan Wang; Andrey Zhdanov; Sergei Bykovskii

doi:10.1117/12.2548061

18 December 2019 Using convolutional neural network for matching cost computation in stereo matching

Aleksei Denisov, Yan Wang, Andrey Zhdanov, Sergei Bykovskii

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 113420P (2019) https://doi.org/10.1117/12.2548061
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

Stereo matching is one of the most important computer vision tasks. Several methods can be used to compute a matching cost of two pictures. This paper proposes a method that uses convolutional neural networks to compute the matching cost. The network architecture is described as well as teaching process. The matching cost metric based on the result of neural network is applied to base method which uses support points grid (ELAS). The proposed method was tested on Middlebury benchmark images and showed an accuracy improvement compared to the base method.

Citation Download Citation

Aleksei Denisov, Yan Wang, Andrey Zhdanov, and Sergei Bykovskii "Using convolutional neural network for matching cost computation in stereo matching", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420P (18 December 2019); https://doi.org/10.1117/12.2548061

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